Lung cancer in Asian women and health system implications for Australia

Toxic effect of cooking oil fume (COF) on lungs: Evidence of endoplasmic reticulum stress in rat

BACKGROUND

Cooking oil fumes (COF) is one of the primary sources of indoor air pollution in China, which is associated with respiratory diseases such as acute lung injury and lung cancer. However, evidence of COF toxic effect was few.

OBJECTIVES

The research was aimed to investigate the toxic effect and the underlying mechanisms induced by COF.

METHODS

The female Wistar rats were randomly divided into several groups, including control group, COF exposure group and VE protection group, and instilled intratracheally with different COF suspensions (0.2, 2, 20 mg/kg) or saline once every 3 days for 30 days. After 24 h of final exposure, all rat were anesthetic euthanasia to draw materials. The alveolar lavage fluid (BALF) was for inflammatory cell count. The lung homogenate was to determine the biochemical indexes such as oxidative stress, apoptosis factors, carcinogenic toxicity and endoplasmic reticulum (ER) stress. The left lung was made for immunohistochemical and histopathological analysis.

RESULTS

The results showed that the levels of oxidative stress (ROS), apoptosis factors (NF-κB), carcinogenic toxicity (P53 and 8-OhdG), ER stress (IRE-1α and Caspase-12) in 2 mg/kg and 20 mg/kg COF exposure groups were significantly increased compared with the saline groups. The above pathological changes were improved after vitamin E (VE) supplementation. In addition, the immunohistochemical and histopathological analysis found the same trend.

CONCLUSION

The COF had health risk of heredity and potential carcinogenicity. Besides, COFs can not only induce oxidative stress, but also induce ER stress in lung and airway epithelial cells of female rats through the unfolded protein reaction (UPR) pathway. It revealed that the oxidative stress and ER stress interacted in aggravating lung injury. VE could effectively alleviate the lung injury causing by COF exposure.

Do ethnic patients report longer lung cancer intervals than Anglo-Australian patients?: Findings from a prospective, observational cohort study

OBJECTIVE

Lung cancer patients from ethnic minorities have poorer outcomes than their Caucasian counterparts. We compared lung cancer intervals between culturally and linguistically diverse (CALD) and Anglo-Australian patients to identify ethnic disparities.

METHODS

This was a prospective, observational cohort study comprising a patient survey and reviews of patients' hospital and general practice records. Across three states, 577 (407 Anglo-Australian and 170 CALD) patients were recruited and their hospital records reviewed. The survey was returned by 189 (135 Anglo-Australian and 54 CALD) patients, and a review was completed by general practitioners (GPs) of 99 (76 Anglo-Australian and 23 CALD) patients. Survival and Cox regression analyses were conducted.

RESULTS

CALD patients had longer hospital diagnostic interval [median 30 days, 95% confidence interval (CI) 26-34] than Anglo-Australian patients (median 17, 95% CI 14-20), p = 0.005, hazard ratio (HR) = 1.32 (95% CI 1.09-1.60). This difference persisted after relevant factors were taken into consideration, adjusted HR = 1.26 (95% CI 1.03-1.54, p = 0.022). CALD patients also reported longer prehospital intervals; however, these differences were not statistically significant.

CONCLUSION

Target interventions need to be developed to address ethnic disparity in hospital diagnostic interval.

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

Background

Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.

Result

Here we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically. Then, we transformed the undirected interaction network to directed network by employing Granger causality test, followed by the predictors screened with the use of the stepwise character selection algorithm. Six predictors, including TOP2A, GRK5, SIRT7, MCM7, EGFR, and COL1A2, were ultimately identified. All the predictors are the cancer-related, and the number is very small fascinating diagnosis. Finally, the validation of this approach was verified by robustness analyses applied to six independent datasets; the precision is up to 95.3% ∼ 100%.

Conclusion

Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new, robust, and effective method for extracting gene predictors. We identified as low as 6 genes which can be taken as predictors for diagnosing lung adenocarcinoma.